Genetic Algorithm is a series of simulation evolutionary algorithms proposed by [1]}, and later summarized by DeJong, Goldberg and others.
The general flowchart of Genetic Algorithm is shown in Figure REF .
The Genetic Algorithm first encodes the problem, then calculates the fitness, then selects the parent and the mother by roulette, and finally generates the children with high fitness by crossover and mutation, and finally generates the individuals with high fitness after many iterations, which is the satisfied solution or optimal solution of the problem.
Simple Genetic Algorithm (SGA) uses single-point crossover and simple mutation to embody information exchange between individuals and local search, and does not rely on gradient information, so SGA can find the global optimal solution.
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